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Lead Solution architect

NexTurn, Inc.United States🇺🇸United States

Quick overview

SeniorityMid Senior
Work modeHybrid
LocationUnited States
OpenStackAnsibleBashCUDAGPTGrafanaHelmLLMPrometheusPythonTerraformVMwareGenerative AIKubernetesLinuxRAG

Job description

Role: Solution Architect- V
Location : Remote
The Solution Architect V is a principal-level, customer-facing architect for Rackspace's largest and most technically demanding GPU-as-a-Service (GPUaaS) opportunities in the United States. It is the final technical authority on strategic GPU and inference pursuits, engaging CTOs, CIOs, Chief AI Officers and heads of ML platform, and shaping multi-rack deals across NVIDIA and AMD Instinct platforms and the managed inference services built on them.
The role sits in the GPUaaS team within Rackspace Private Cloud, which designs and sells dedicated and multi-tenant GPU capacity (bare metal, managed Kubernetes, OpenStack and VMware) together with managed inference services. It leads and mentors Solution Architects and Engineers, and works with product management on reference architectures and the roadmap.
Career Level Summary
  • Recognized as an expert within the company, with in-depth expertise in own discipline and broad knowledge of related disciplines
  • Solves unique problems with broad business impact, and contributes to sub-function strategy
  • Critical Competencies
  • Excellence: Accountability, discipline and a proven record of exceptional results.
  • Customer-driven: Proactive, collaborative problem-solving focused on customer success.
  • Expertise: Deep understanding of customer needs, with continuously growing skills.
  • Agility: Responds quickly to changing customer needs with innovative solutions.
  • Compassion: Builds trust and a supportive environment inside and outside Rackspace.
Key Responsibilities
Customer and deal leadership
  • Serve as senior technical lead and trusted advisor on strategic US GPUaaS accounts, from early strategy conversations through technical close.
  • Present solutions, business cases and roadmaps to C-level stakeholders at Fortune 500 and regulated organizations (healthcare, financial services, public sector, energy).
  • Build and defend TCO and value models against hyperscalers, other GPU clouds and on-premises builds, within deal-desk and pricing guardrails.
  • Lead the technical part of large, multi-workstream bids and RFPs.
  • Infrastructure and inference design
  • Architect large-scale GPU environments on NVIDIA and AMD Instinct: fabrics, storage, rack power and liquid cooling, multi-tenant isolation and delivery-model selection.
  • Design production inference platforms sized to TTFT, throughput, concurrency and cost-per-token targets.
  • Define and oversee POCs and benchmarks with agreed success criteria, and present reproducible results to customer executives.
  • Design sovereign, private and air-gapped deployments with partners such as Palantir and Uniphore, and validate power, capacity and timelines with engineering and data center teams before commitment.
Team and ecosystem
  • Lead and mentor Solution Architects and Engineers, and coach Sales on GPUaaS positioning and qualification.
  • Act as primary technical contact with NVIDIA, AMD, OEM and ISV partners on joint pursuits and co-selling.
  • Build reusable reference architectures, sizing tools and benchmark playbooks, and feed field insight into the product roadmap.
Key Performance Indicators
  • Technical win rate and deal close rate on strategic pursuits
  • Average deal size and complexity (committed GPU capacity, contract value)
  • POC conversion rate and time to technical decision
  • Quality of sold designs and delivery outcomes
  • Quality of reusable assets, and 360° feedback from peers, Sales and partners
Required Experience
  • 12+ years in enterprise infrastructure, cloud or solution architecture, including customer-facing pre-sales or consulting roles.
  • GPU infrastructure (mandatory): 4+ years hands-on designing, deploying or operating GPU clusters.
  • GPU selling: 1+ years selling GPU capacity, GPU cloud or AI infrastructure, with technical wins on GPU commitments.
  • AI data center: Understanding of AI data center nuances (high-density power, liquid cooling, power availability and lead times, facility readiness) and how they constrain GPU design, cost and timelines.
  • Scaling AI workloads: Experience scaling production AI workloads from single node to multi-node and multi-cluster, including autoscaling, multi-tenant GPU sharing and capacity planning.
  • LLM inference: 2+ years deploying and operating LLM inference in production (such as vLLM, SGLang, TensorRT-LLM or NIM).
  • Hands-on coding: Strong, current hands-on coding in Python, plus Bash and Infrastructure as Code (Terraform, Ansible, Helm). Personally builds POCs, benchmark harnesses, sizing tools and inference deployments, not only designs them.
  • Hands-on technical skills: Personally deploys and troubleshoots GPU environments: Linux administration, GPU drivers and CUDA/ROCm, containers, Kubernetes (GPU Operator, Helm), standing up and tuning serving engines (vLLM, SGLang), running benchmarks (GenAI-Perf, NCCL tests) and monitoring (DCGM, Prometheus, Grafana).
Technical Expertise
GPU infrastructure (expert depth expected)
  • Accelerators and their sizing characteristics (HBM, memory bandwidth, scale-up domain, power): NVIDIA H100/H200, B200/B300, GB200/GB300 NVL72; AMD Instinct MI300X/MI325X/MI355X.
  • Networking and storage: NVLink/NVSwitch, InfiniBand and RoCEv2/Spectrum-X, SuperNICs/DPUs; parallel and all-flash file systems (WEKA, VAST, DDN) and GPUDirect Storage.
  • Orchestration and multi-tenancy: Kubernetes with NVIDIA GPU Operator and AMD ROCm equivalents, MIG, time-slicing, bare-metal provisioning on OpenStack, tenant isolation and quotas.
  • Data center and validation: 40 kW to 140 kW+ racks, direct-to-chip liquid cooling, burn-in and acceptance testing (DCGM, NCCL/RCCL), NVIDIA and AMD reference architectures.
Inference (expert depth expected)
  • Serving engines: vLLM, SGLang, TensorRT-LLM, Triton and NIM, AMD ROCm stack; KServe and Gateway API Inference Extension; disaggregated serving (NVIDIA Dynamo, llm-d).
  • Optimization: continuous batching, KV-cache management, prefix caching, speculative decoding and quantization (FP8, FP4, INT4).
  • Sizing and benchmarking: GPU count, type and parallelism (tensor, pipeline, expert) to model, context length, traffic and SLA; TTFT, inter-token latency, goodput and cost per million tokens.
  • Open-weight models (Llama, Qwen, DeepSeek, Mistral, gpt-oss), including MoE and long-context models, plus RAG, agentic and model-gateway patterns.
  • Knowledge and Skills
  • AI infrastructure and inference market, GPU cloud economics and competitive positioning (hyperscalers and neoclouds)
  • Compliance requirements for AI deployments, including regulated industries such as healthcare, financial services, insurance, public sector and energy sectors
  • Executive communication, solution selling, negotiation, proposal and RFP response, and commercial design (TCO, pricing inputs, SOW scoping)
  • Coaching, mentoring and partner management
Education and Certifications
  • Bachelor's degree in Computer Science, Engineering or a related field, or an equivalent combination of education and experience.
  • At least two NVIDIA-Certified Professional credentials (NCP-AII, NCP-AIN, NCP-AIO or NCP-GENL), preferred at hire or achieved within 12 months.
  • Desirable: CKA, AMD Instinct/ROCm, Red Hat OpenShift AI or OpenStack, cloud architect, Palantir Foundry/AIP and Rackspace GPUaaS certifications.
  • Travel and Physical Demands
  • Up to 40% travel, primarily within the United States, to customer sites, partner facilities, events and data centers.
  • Data center visits involve extended standing and walking, noise and site safety requirements. Reasonable accommodations may be made for individuals with disabilities.

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